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To Shoot Or Not To Shoot

To Shoot Or Not To Shoot . To Shoot Or Not To Shoot . Since 1900, 10% to 25% of US war fatalities in resulted from fratricide. Targeting Decisions: Possible Outcomes . Soldier and CID detect a friend. 2) Soldier and CID fail to detect a friend.

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To Shoot Or Not To Shoot

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  1. To Shoot Or Not To Shoot

  2. To Shoot Or Not To Shoot Since 1900, 10% to 25% of US war fatalities in resulted from fratricide

  3. Targeting Decisions: Possible Outcomes • Soldier and CID detect a friend. • 2) Soldier and CID fail to detect a friend. • 3) Soldier detects a friend and CID fails to detect a friend. • 4) Soldier fails to detect a friend and CID detects a friend.

  4. Automation Usage Decisions (AUDs) AUDs- Choices in which a human operator has the option of relying upon manual control or one or more levels of automation (LOAs) to perform a task. Optimal AUD-Soldier relies upon the form of control that is most likely to result in a correct decision.

  5. Types of Suboptimal AUDs Misuse is over reliance, soldier employs automation when manual control or a relatively low LOA has a greater likelihood of success Disuse is the under utilization of automation, soldier manually performs a task that could best be done by a machine or a higher LOA. Perform the actions necessary to accomplish the objective via automated or manual control.

  6. Beck, Dzindolet, & Pierce (2002) Appraisal Errors-Soldier misjudges the relative utilities of the automated (CID) and non-automated (e.g., view through gun site) options. Intent Errors-Soldier disregards the utilities of the alternatives when making AUDs.

  7. Intent Errors: Two Images of an Operator An operator is a single-minded individual whose sole object is to maximize task performance An operator‘s decision to rely on automation is based on a number of contingencies only one of which is to achieve a successful performance. Perform the actions necessary to accomplish the objective via automated or manual control.

  8. John Henry Effect John Henry Effect: Operators respond to automation as a challenger, competitor, or threat Increasing the operator’s personal involvement with the non-automated alternative augments the likelihood of a John Henry Effect.

  9. John Henry Effect Variables that increase the strength of a John Henry Effect augment operators‘ preference for the non-automated over the automated alternative Heightened preference for the non-automated option should: 1) increase disuse and 2) decrease misuse

  10. Design 2 (Operator: Self-reliant, Other-reliant) x 2 (Machine Performance: Inferior, Superior) x 14 (Trial Blocks) design Dependent Variable: Suboptimal AUDs (Superior Machine: Basing credit point on the operator’s performance; Inferior Machine: Basing credit on the machine’s performance)

  11. Credit Choice Screen

  12. Sample Helicopter Photograph

  13. Sample Helicopter Photograph

  14. Operator Response Screen

  15. CID Response Screen

  16. Results Screen

  17. Hypotheses • Self-reliant operators will be less likely to base credit points on the CID than other-reliant operators • Therefore • Disuse will be greater in the self-superior than in the other-superior condition • Misuse will be higher among other-inferior than self-inferior persons

  18. Disuse • Figure 1. Mean suboptimal automation usage decisions (AUDs) as a function of operator and trial block for persons working with the superior machine.

  19. Misuse • Figure 2. Mean suboptimal automation usage decisions (AUDs) as a function of operator and trial block for persons working with the inferior machine.

  20. Conclusions • 1) Self-reliant and other-reliant operators were yoked. Each had the same information. It seems reasonable to conclude that the difficulty in determining the optimal AUD was approximately equal in both conditions. Thus, the large differences in suboptimal AUDs were probably due to intent rather than appraisal errors. • 2)Results support the hypotheses that factors which augment the degree of personal involvement or challenge from automated devices will increase the probability of disuse and decrease the likelihood of misuse

  21. A Few Implications • Operator training programs should attempt to attenuate intent as well as appraisal errors. • At least on this task, intent errors were a significant source of suboptimal AUDs • Both appraisal and intent errors are sufficient to produce suboptimal AUDs although neither is necessary • It will be a hollow achievement if advances in our knowledge of hardware and software is matched by an equally sophisticated comprehension of the causes and control of misuse and disuse.

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